
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
plt.rcParams['figure.figsize'] = [12, 6]
plt.rcParams.update({'font.size': 12})


df = pd.read_excel('columns.xlsx')
col = df['Symbol'].values
col = col.tolist()


df_stableFeedFlow = pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/NOC_stableFeedFlow_outputs.csv',header=None)
df_stableFeedFlow.set_index = df_stableFeedFlow.iloc[:,0]
df_stableFeedFlow = df_stableFeedFlow.drop(columns=0)
df_stableFeedFlow.columns= col
df_stableFeedFlow.sample(5)


df_varyingFeedFlow=pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/NOC_varyingFeedFlow_outputs.csv',header=None)
df_varyingFeedFlow.set_index = df_varyingFeedFlow.iloc[:,0]
df_varyingFeedFlow = df_varyingFeedFlow.drop(columns=0)
df_varyingFeedFlow.columns= col
df_varyingFeedFlow.sample(5)


df_deltaP_increase = pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/deltaP_increase_outputs.csv',header=None)
df_deltaP_increase.set_index = df_deltaP_increase.iloc[:,0]
df_deltaP_increase = df_deltaP_increase.drop(columns=0)
df_deltaP_increase.columns= col
df_deltaP_increase.sample(5)


df_deltaP_increase.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1440 entries, 0 to 1439
Data columns (total 46 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   F3           1440 non-null   float64
 1   Tatm         1440 non-null   float64
 2   T1           1440 non-null   float64
 3   P4           1440 non-null   float64
 4   deltaP       1440 non-null   float64
 5   P6           1440 non-null   int64  
 6   Fair         1440 non-null   float64
 7   T3           1440 non-null   float64
 8   T2           1440 non-null   float64
 9   Tr           1440 non-null   float64
 10  Treg         1440 non-null   float64
 11  Lsp          1440 non-null   float64
 12  Tcyc         1440 non-null   float64
 13  Tcyc - Treg  1440 non-null   float64
 14  Cco,sg       1440 non-null   int64  
 15  Co2,sg       1440 non-null   float64
 16  P5           1440 non-null   float64
 17  V4           1440 non-null   float64
 18  V6           1440 non-null   float64
 19  V7           1440 non-null   float64
 20  V3           1440 non-null   float64
 21  V1           1440 non-null   float64
 22  V2           1440 non-null   float64
 23  Frgc         1440 non-null   int64  
 24  Fsc          1440 non-null   int64  
 25  ACAB         1440 non-null   float64
 26  AWGC         1440 non-null   float64
 27  F5           1440 non-null   float64
 28  F7           1440 non-null   float64
 29  Fsg          1440 non-null   float64
 30  FV11         1440 non-null   int64  
 31  P1           1440 non-null   float64
 32  P2           1440 non-null   float64
 33  FLPG         1440 non-null   float64
 34  FLN          1440 non-null   float64
 35  FHN          1440 non-null   float64
 36  FLCO         1440 non-null   float64
 37  FSlurry      1440 non-null   float64
 38  FReflux      1440 non-null   float64
 39  Tfra         1440 non-null   float64
 40  T10          1440 non-null   float64
 41  T20          1440 non-null   float64
 42  V9           1440 non-null   float64
 43  V8           1440 non-null   float64
 44  V10          1440 non-null   float64
 45  V11          1440 non-null   float64
dtypes: float64(41), int64(5)
memory usage: 517.6 KB


df_deltaP_increase.describe().T


sns.heatmap(df_deltaP_increase.corr(),cmap='coolwarm')

<AxesSubplot:>


for n,i in enumerate(df_stableFeedFlow.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_stableFeedFlow[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


for n,i in enumerate(df_varyingFeedFlow.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_varyingFeedFlow[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


for n,i in enumerate(df_deltaP_increase.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_deltaP_increase[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


from sklearn.preprocessing import StandardScaler
ss = StandardScaler()


X = ss.fit_transform(df_stableFeedFlow)


from sklearn.decomposition import PCA
pca = PCA()


X_pca = pca.fit_transform(X)


plt.figure(figsize=(15,6))
sns.set_style('whitegrid')
sns.lineplot(x=list(range(1,47)), y=np.cumsum(pca.explained_variance_ratio_), drawstyle='steps-pre')
sns.lineplot(x=list(range(0,46)),y=np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Eigen Values')
plt.ylabel('Ratio of Variance')
plt.title('Variance by each Eigen Value')
plt.show()


pca = PCA(n_components=10)
X_pca = pca.fit_transform(X)


plt.figure(figsize=(15,6))
sns.set_style('whitegrid')
sns.lineplot(x=list(range(1,11)), y=np.cumsum(pca.explained_variance_ratio_), drawstyle='steps-pre')
sns.lineplot(x=list(range(0,10)),y=np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Eigen Values')
plt.ylabel('Ratio of Variance')
plt.title('Variance by each Eigen Value')
plt.show()


X_train = X.reshape(2880,46,1)


# del model


model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256,return_sequences=True),input_shape=(46,1)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128,return_sequences=True)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 bidirectional (Bidirection  (None, 46, 512)           528384    
 al)                                                             
                                                                 
 dropout (Dropout)           (None, 46, 512)           0         
                                                                 
 bidirectional_1 (Bidirecti  (None, 46, 256)           656384    
 onal)                                                           
                                                                 
 dropout_1 (Dropout)         (None, 46, 256)           0         
                                                                 
 dense (Dense)               (None, 46, 1)             257       
                                                                 
=================================================================
Total params: 1185025 (4.52 MB)
Trainable params: 1185025 (4.52 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________


model.fit(X_train,X_train,epochs=30)

Epoch 1/30
90/90 [==============================] - 29s 249ms/step - loss: 0.1533 - mae: 0.2153
Epoch 2/30
90/90 [==============================] - 22s 248ms/step - loss: 0.0031 - mae: 0.0389
Epoch 3/30
90/90 [==============================] - 22s 247ms/step - loss: 0.0025 - mae: 0.0345
Epoch 4/30
90/90 [==============================] - 29s 318ms/step - loss: 0.0023 - mae: 0.0321
Epoch 5/30
90/90 [==============================] - 37s 415ms/step - loss: 0.0021 - mae: 0.0305
Epoch 6/30
90/90 [==============================] - 37s 411ms/step - loss: 0.0019 - mae: 0.0292
Epoch 7/30
90/90 [==============================] - 37s 414ms/step - loss: 0.0018 - mae: 0.0284
Epoch 8/30
90/90 [==============================] - 37s 414ms/step - loss: 0.0018 - mae: 0.0277
Epoch 9/30
90/90 [==============================] - 37s 413ms/step - loss: 0.0017 - mae: 0.0272
Epoch 10/30
90/90 [==============================] - 37s 413ms/step - loss: 0.0017 - mae: 0.0267
Epoch 11/30
90/90 [==============================] - 37s 413ms/step - loss: 0.0017 - mae: 0.0267
Epoch 12/30
90/90 [==============================] - 37s 415ms/step - loss: 0.0016 - mae: 0.0260
Epoch 13/30
90/90 [==============================] - 38s 419ms/step - loss: 0.0016 - mae: 0.0258
Epoch 14/30
90/90 [==============================] - 37s 416ms/step - loss: 0.0016 - mae: 0.0257
Epoch 15/30
90/90 [==============================] - 37s 416ms/step - loss: 0.0015 - mae: 0.0254
Epoch 16/30
90/90 [==============================] - 37s 416ms/step - loss: 0.0015 - mae: 0.0252
Epoch 17/30
90/90 [==============================] - 38s 418ms/step - loss: 0.0015 - mae: 0.0251
Epoch 18/30
90/90 [==============================] - 37s 417ms/step - loss: 0.0015 - mae: 0.0250
Epoch 19/30
90/90 [==============================] - 38s 420ms/step - loss: 0.0015 - mae: 0.0248
Epoch 20/30
90/90 [==============================] - 38s 426ms/step - loss: 0.0015 - mae: 0.0248
Epoch 21/30
90/90 [==============================] - 37s 412ms/step - loss: 0.0014 - mae: 0.0245
Epoch 22/30
90/90 [==============================] - 37s 416ms/step - loss: 0.0014 - mae: 0.0244
Epoch 23/30
90/90 [==============================] - 38s 421ms/step - loss: 0.0014 - mae: 0.0245
Epoch 24/30
90/90 [==============================] - 37s 415ms/step - loss: 0.0014 - mae: 0.0245
Epoch 25/30
90/90 [==============================] - 39s 439ms/step - loss: 0.0014 - mae: 0.0243
Epoch 26/30
90/90 [==============================] - 40s 448ms/step - loss: 0.0014 - mae: 0.0242
Epoch 27/30
90/90 [==============================] - 37s 413ms/step - loss: 0.0014 - mae: 0.0245
Epoch 28/30
90/90 [==============================] - 37s 410ms/step - loss: 0.0014 - mae: 0.0242
Epoch 29/30
90/90 [==============================] - 37s 411ms/step - loss: 0.0014 - mae: 0.0241
Epoch 30/30
90/90 [==============================] - 37s 414ms/step - loss: 0.0014 - mae: 0.0243

<keras.src.callbacks.History at 0x17996651e50>


error_ae = []
for i in range(X.shape[0]):
    y_pred = model.predict(X[i].reshape(1,46,1),verbose=None)[0,:,0]
    error_ae.append(np.abs(X[i]-y_pred).sum())
AE_CL = np.percentile(error_ae,99)


pd.Series(error_ae).plot()
plt.hlines(AE_CL,0,len(error_ae),colors='red',linestyles='--')

<matplotlib.collections.LineCollection at 0x1799aefc430>


X_reconstructed = np.dot(X_pca,pca.components_)
error_pca = X-X_reconstructed
Q_train = np.sum(np.abs(error_pca),axis=1)
Q_CL = np.percentile(Q_train,99)
# Q_train plot with CL
plt.figure()
plt.plot(Q_train, color='black')
plt.plot([1,len(Q_train)],[Q_CL,Q_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('Q metric: training data')
plt.title(f'Q metrix is max: {Q_train.max()} at:{Q_train.argmax()}mins')
plt.show()


lambda_ = np.diag(pca.explained_variance_)
lambda_inv = np.linalg.inv(lambda_)
T_train = np.zeros(X_pca.shape[0])
for i in range(X_pca.shape[0]):
    T_train[i] = np.dot(np.dot(X_pca[i],lambda_inv),X_pca[i].T)
T_CL = np.percentile(T_train,99)
# T2_train plot with CL
plt.figure()
plt.plot(T_train, color='black')
plt.plot([1,len(T_train)],[T_CL,T_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('T$^2$ metric: training data')
plt.title(f'T$^2$ metrix is max: {np.array(T_train).max()} at:{np.array(T_train).argmax()}mins')
plt.show()


cosine = []
ed = []
for i in range(X.shape[0]):
    v1 = X[i]
    v2 = np.dot(X_pca,pca.components_)[i]
    cosine.append(np.dot(v1,v2)/(np.linalg.norm(v1)*np.linalg.norm(v2)))
    ed.append(np.linalg.norm(v1 - v2))
C_CL = np.min(cosine)
E_CL = np.percentile(ed,99)
# pd.Series(ed).plot(color='black')
# plt.plot([1,len(ed)],[E_CL,E_CL], linestyle='--',color='red', linewidth=2)
# plt.show()
pd.Series(cosine).plot(color='black')
plt.plot([1,len(cosine)],[C_CL,C_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('Cosine similarity metric: training data')
plt.title(f'Cosine Similarity')
plt.show()


Q_CL,T_CL,C_CL,E_CL,AE_CL

(4.123113215519074,
 20.424350352568812,
 0.9427550112367162,
 0.9281694746755802,
 0.4382286902947803)


def Q_test(X,X_pca,pca_components_,Q_CL):
    X_reconstructed = np.dot(X_pca,pca_components_)
    error_pca = X-X_reconstructed
    Q_train = np.sum(np.abs(error_pca),axis=1)
    # Q_train plot with CL
    plt.figure()
    plt.plot(Q_train, color='black')
    plt.plot([1,len(Q_train)],[Q_CL,Q_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('Q metric: training data')
    plt.title(f'Q metrix is max: {Q_train.max()} at:{Q_train.argmax()}mins')
    plt.show()
    return error_pca


def T_test(X_pca,explained_variance_,TCL):
    lambda_ = np.diag(pca.explained_variance_)
    lambda_inv = np.linalg.inv(lambda_)
    T_train = np.zeros(X_pca.shape[0])
    for i in range(X_pca.shape[0]):
        T_train[i] = np.dot(np.dot(X_pca[i],lambda_inv),X_pca[i].T)
    # T2_train plot with CL
    plt.figure()
    plt.plot(T_train, color='black')
    plt.plot([1,len(T_train)],[T_CL,T_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('T$^2$ metric: training data')
    plt.title(f'T$^2$ metrix is max: {np.array(T_train).max()} at:{np.array(T_train).argmax()}mins')
    plt.show()


def cosine(X,X_transformed,pca_components_,C_CL,E_CL):
    cosine = []
    ed = []
    for i in range(X.shape[0]):
        v1 = X[i]
        v2 = np.dot(X_transformed,pca_components_)[i]
        cosine.append(np.dot(v1,v2)/(np.linalg.norm(v1)*np.linalg.norm(v2)))
        ed.append(np.linalg.norm(v1 - v2))
#     pd.Series(ed).plot(color='black')
#     plt.plot([1,len(ed)],[E_CL,E_CL], linestyle='--',color='red', linewidth=2)
#     plt.xlabel('Sample #')
#     plt.ylabel('Eucledian Distance metric: training data')
#     plt.show()
    pd.Series(cosine).plot(color='black')
    plt.plot([1,len(cosine)],[C_CL,C_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('Cosine similarity metric: training data')
    plt.title(f'Cosine Similarity')
    plt.show()


def autoencoder(df_test,CL):
    X_test = ss.transform(df_test)
    error_ae = []
    error_sum = []
    for i in range(X_test.shape[0]):
        y_pred = model.predict(X_test[i].reshape(1,46,1),verbose=None)[0,:,0]
        error_ae.append(np.abs(X_test[i]-y_pred))
        error_sum.append(np.abs(X_test[i]-y_pred).sum())
    error_ae=np.array(error_ae)
    pd.Series(error_sum).plot(color = 'black')
    plt.hlines(CL,0,len(error_ae),colors='red',linestyles='--')
    plt.xlabel('Sample #')
    plt.ylabel('Reconstruction error by Autoencoder')
    return error_ae


X = ss.transform(df_varyingFeedFlow)
X_test = pca.transform(X)


error_pca = Q_test(X,X_test,pca.components_,Q_CL)


T_test(X_test,pca.explained_variance_,T_CL)


cosine(X,X_test,pca.components_,C_CL,E_CL)


error_ae = autoencoder(df_varyingFeedFlow,AE_CL)


X = ss.transform(df_deltaP_increase)
X_test = pca.transform(X)


error_pca = Q_test(X,X_test,pca.components_,Q_CL)


T_test(X_test,pca.explained_variance_,T_CL)


cosine(X,X_test,pca.components_,C_CL,E_CL)


error_ae = autoencoder(df_deltaP_increase,AE_CL)


#%% Q contribution
error = np.abs(error_pca).sum(axis=1)
cum = []
for index,value in enumerate(error):
    if (value>Q_CL) and (len(cum)<15):
        previous_val = value
        cum.append(value)
        if len(cum) == 15:
            sample = index
            break
    else:
        cum=[]
# sample = ((pd.Series(error_pca.sum(axis=1))-pd.Series(error_pca.sum(axis=1)).shift()).abs()).argmax()
print('Time-',sample,'mins')
error_test_sample = error_pca[sample]
Q_contri = np.abs(error_test_sample) # *error_test_sample # vector of contributions

Time- 146 mins


plt.figure(figsize=[15,4])
plt.bar(['variable ' + str((i+1)) for i in range(len(Q_contri))], Q_contri)
plt.xticks(rotation = 80)
plt.ylabel('Q contributions')
plt.show()


plt.figure(figsize=(15,40))
print('Time-',sample,'mins')
for i,n in enumerate(np.argsort(Q_contri)[:-11:-1]):
    plt.subplot(5,2,i+1)
    plt.plot(df_deltaP_increase.iloc[:,n],'blue', linewidth=1)
    plt.xlabel('time (mins)')
    plt.ylabel(df['Symbol'][n])
    plt.title(df['Description'][n])
    plt.show

Time- 146 mins


#%% Autoencoder Error
error = np.abs(error_ae).sum(axis=1)
cum = []
for index,value in enumerate(error):
    if (value>AE_CL) and (len(cum)<15):
        previous_val = value
        cum.append(value)
        if len(cum) == 15:
            sample = index
            break
    else:
        cum=[]
# sample = ((pd.Series(error_ae.sum(axis=1))-pd.Series(error_ae.sum(axis=1)).shift()).abs()).argmax()
print('Time-',sample,'mins')
error_test_sample = error_pca[sample]
Q_contri = np.abs(error_test_sample) # *error_test_sample # vector of contributions

Time- 198 mins


plt.figure(figsize=(15,45))
print('Time-',sample,'mins')
for i,n in enumerate(np.argsort(error_ae[sample])[:-11:-1]):
    plt.subplot(5,2,i+1)
    plt.plot(df_deltaP_increase.iloc[:,n],'blue', linewidth=1)
    plt.xlabel('time (mins)')
    plt.ylabel(df['Symbol'][n])
    plt.title(df['Description'][n])
    plt.show

Time- 198 mins

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
1801	165.06	78.821	460.53	34.4	-6.4	28	2.6793	1566.7	615.99	968.96	...	1644.9	218.89	3006.8	314.30	509.93	628.46	46.668	49.932	50.217	47.279
634	164.79	79.947	461.09	34.4	-6.4	28	2.6799	1561.6	616.01	969.05	...	1645.1	213.78	2993.1	314.71	509.75	628.01	46.003	49.634	49.512	47.311
2183	165.06	80.040	460.64	34.4	-6.4	28	2.6831	1566.0	616.00	969.03	...	1647.3	209.26	3001.1	314.86	509.84	628.04	45.829	49.743	49.279	47.429
2610	165.20	78.028	460.16	34.4	-6.4	28	2.6835	1569.8	615.99	969.01	...	1644.6	208.76	2986.8	313.36	509.92	628.18	47.757	49.760	48.978	47.269
1803	165.03	78.832	460.24	34.4	-6.4	28	2.6795	1568.2	615.99	968.97	...	1644.1	218.69	3005.7	314.29	509.92	628.44	46.681	49.915	50.222	47.233

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
1193	168.40	77.862	460.78	34.4	-6.4	28	2.7362	1585.9	616.00	969.00	...	1699.1	205.70	3517.3	322.11	513.91	633.47	39.600	56.977	52.924	50.034
5673	162.66	76.135	461.16	34.4	-6.4	28	2.6426	1548.0	616.00	969.01	...	1606.8	221.66	2614.5	305.18	506.66	623.40	56.834	44.856	47.081	45.430
7041	165.80	76.944	460.74	34.4	-6.4	28	2.6944	1570.0	616.01	969.02	...	1652.2	212.57	3081.2	314.41	510.76	629.54	46.994	51.151	49.849	47.619
5056	162.08	80.012	460.68	34.4	-6.4	28	2.6346	1547.3	615.99	969.03	...	1591.9	230.72	2575.0	307.03	505.85	622.36	53.811	43.865	47.206	44.670
4831	162.04	79.589	460.43	34.4	-6.4	28	2.6340	1548.6	616.01	969.03	...	1590.8	230.83	2564.0	306.53	505.78	622.23	54.441	43.762	47.107	44.615

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
483	165.15	79.501	460.62	34.8	-6.8	28	2.6814	1566.7	616.00	968.97	...	1649.6	211.56	3005.2	314.62	509.89	628.20	46.184	49.852	49.831	47.558
33	165.03	75.438	461.00	34.4	-6.4	28	2.6807	1563.6	616.00	969.00	...	1640.7	213.97	2945.9	310.97	509.80	628.09	50.679	49.492	48.761	47.057
724	165.21	80.059	460.27	34.8	-6.8	28	2.6856	1569.2	616.00	969.04	...	1647.8	206.93	3018.9	315.16	510.01	628.25	45.523	49.988	49.071	47.443
1153	164.97	78.208	460.59	34.8	-6.8	28	2.6783	1565.8	616.00	968.97	...	1639.3	216.10	2968.5	313.23	509.71	628.03	47.883	49.475	49.367	46.980
1245	165.05	77.363	460.22	34.8	-6.8	28	2.6835	1568.6	616.01	969.04	...	1647.2	211.48	2996.3	313.13	510.04	628.42	48.124	49.958	49.234	47.405

	count	mean	std	min	25%	50%	75%	max
F3	1440.0	165.012694	1.582447e-01	164.53000	164.890000	165.020000	165.140000	165.480000
Tatm	1440.0	78.342193	1.491834e+00	74.98100	77.191500	78.746000	79.687250	80.070000
T1	1440.0	460.899750	3.939347e-01	459.58000	460.650000	460.910000	461.140000	462.050000
P4	1440.0	34.732485	1.372139e-01	34.40000	34.800000	34.800000	34.800000	34.800000
deltaP	1440.0	-6.732483	1.372164e-01	-6.80000	-6.800000	-6.800000	-6.800000	-6.400000
P6	1440.0	28.000000	0.000000e+00	28.00000	28.000000	28.000000	28.000000	28.000000
Fair	1440.0	2.680606	2.895453e-03	2.67220	2.678575	2.680800	2.682800	2.687600
T3	1440.0	1564.149444	2.865294e+00	1555.80000	1562.200000	1564.100000	1566.000000	1573.500000
T2	1440.0	616.000063	7.019004e-03	615.98000	616.000000	616.000000	616.000000	616.020000
Tr	1440.0	968.996868	3.865328e-02	968.82000	968.980000	969.000000	969.020000	969.250000
Treg	1440.0	1250.000000	4.875090e-02	1249.90000	1250.000000	1250.000000	1250.000000	1250.100000
Lsp	1440.0	29.807753	2.940493e-01	29.46500	29.651000	29.733000	29.829250	31.453000
Tcyc	1440.0	1255.280000	5.018382e-02	1255.10000	1255.300000	1255.300000	1255.300000	1255.400000
Tcyc - Treg	1440.0	5.280165	3.503803e-02	5.14660	5.256475	5.282450	5.303500	5.354700
Cco,sg	1440.0	29883.704167	5.020733e+01	29756.00000	29852.000000	29883.000000	29913.250000	30112.000000
Co2,sg	1440.0	0.012481	1.555107e-04	0.01191	0.012373	0.012489	0.012586	0.012819
P5	1440.0	24.900000	6.752501e-13	24.90000	24.900000	24.900000	24.900000	24.900000
V4	1440.0	46.261990	3.630540e-01	45.37900	46.008750	46.372500	46.558000	46.840000
V6	1440.0	24.800935	8.767327e-02	24.53000	24.746000	24.809000	24.862250	25.002000
V7	1440.0	54.592518	5.902779e-02	54.42100	54.551000	54.595500	54.637000	54.735000
V3	1440.0	46.458697	2.311408e-01	46.29400	46.330000	46.346000	46.402500	46.995000
V1	1440.0	57.939251	2.065041e-01	57.34300	57.801000	57.934000	58.073000	58.619000
V2	1440.0	45.866167	3.305920e-01	45.03700	45.750000	46.026000	46.077000	46.211000
Frgc	1440.0	49584.829167	5.778035e+01	49414.00000	49544.750000	49589.000000	49628.250000	49742.000000
Fsc	1440.0	49584.416667	5.891687e+01	49396.00000	49544.000000	49588.000000	49627.250000	49831.000000
ACAB	1440.0	280.753229	1.339416e+00	277.20000	279.750000	281.145000	281.830000	282.770000
AWGC	1440.0	204.616125	2.498677e+00	198.60000	202.837500	205.390000	206.670000	208.490000
F5	1440.0	1990.668125	7.094397e+00	1970.20000	1985.900000	1990.500000	1995.300000	2014.000000
F7	1440.0	3737.047153	5.024231e+00	3722.70000	3733.600000	3737.200000	3740.700000	3747.800000
Fsg	1440.0	160.836479	1.739822e-01	160.33000	160.710000	160.850000	160.970000	161.260000
FV11	1440.0	28114.860417	2.735492e+02	27445.00000	27925.000000	28199.000000	28337.000000	28547.000000
P1	1440.0	14.638000	7.640988e-14	14.63800	14.638000	14.638000	14.638000	14.638000
P2	1440.0	35.042184	2.132075e-02	35.00000	35.028000	35.038000	35.056000	35.105000
FLPG	1440.0	3037.619167	4.410832e+01	2931.30000	3006.075000	3051.150000	3073.900000	3106.600000
FLN	1440.0	3918.058750	4.528881e+01	3853.20000	3878.700000	3905.050000	3953.225000	4029.600000
FHN	1440.0	708.819583	3.444213e+00	697.97000	706.727500	708.720000	711.040000	717.330000
FLCO	1440.0	1643.684931	3.785560e+00	1632.90000	1640.800000	1643.700000	1646.500000	1652.200000
FSlurry	1440.0	213.935750	3.735500e+00	205.90000	211.105000	214.095000	216.560000	224.080000
FReflux	1440.0	2984.278819	1.873678e+01	2938.60000	2972.300000	2986.800000	2996.200000	3029.200000
Tfra	1440.0	313.544986	1.203643e+00	310.71000	312.657500	313.910000	314.560000	315.330000
T10	1440.0	509.834722	1.324477e-01	509.50000	509.740000	509.835000	509.930000	510.150000
T20	1440.0	628.158278	1.590511e-01	627.84000	628.030000	628.140000	628.280000	628.470000
V9	1440.0	47.514651	1.504642e+00	45.38300	46.216000	47.063500	48.650500	51.247000
V8	1440.0	49.686217	1.915637e-01	49.28700	49.533750	49.693000	49.844000	50.131000
V10	1440.0	49.368616	4.827719e-01	47.87600	49.071000	49.350500	49.679000	50.565000
V11	1440.0	47.222166	2.078380e-01	46.63000	47.067000	47.227500	47.379000	47.687000

Increase in Reactor Regenerator Pressure Difference¶

Introduction¶

Data Preprocessing¶

Exploratory Data Analysis (EDA)¶

Anomaly Detection Techniques¶

Data Cleaning, Feature Engineering, Predictive Modeling¶

Testing the model on Varying feed flow rate.¶

Testing the model on abnormal dataset.¶

Visualization¶

Conclusion¶

Future Work¶

*Increase in Reactor Regenerator Pressure Difference*¶

Introduction¶

Data Preprocessing¶

Exploratory Data Analysis (EDA)¶

Anomaly Detection Techniques¶

Data Cleaning, Feature Engineering, Predictive Modeling¶

Testing the model on Varying feed flow rate.¶

Testing the model on abnormal dataset.¶

Visualization¶

Conclusion¶

Future Work¶

Increase in Reactor Regenerator Pressure Difference¶